Bias-minimizing Filters for Motion Estimation

Among the myriad of techniques used in estimating motion vector fields, perhaps the most popular and accurate methods are the so called gradient-based methods. A critical step in the gradient-based estimation process is the estimation of image gradients using derivative filters. It is well known that the gradientbased estimators contain significant deterministic bias relating the gradient calculation. In this paper, we describe the fundamental relationship between estimator bias and derivative filters. From this, we suggest an image adaptive method addressing the design of biasminimizing gradient filters. Simulations validate the superior performance of such filters for the many variants of gradient-based estimation including the widely used multiscale iterative methods.